A research partner that shows its evidence, not just an answer.
AetherCV reads computer vision literature the way a careful colleague would — following what cites what, what extends what, and what actually supports each claim.
How does DINOv2 differ from the original DINO in its training objective?
Both methods rely on self-distillation between a student and a slowly-updated teacher network, but DINOv2 extends the original recipe with a larger, automatically curated pretraining corpus and added regularization terms that stabilize training at scale, rather than changing the core distillation loss itself.
What AetherCV is
A reading environment for computer vision research — not a chat window, and not a dashboard. Ask a real question and AetherCV traces it back through the literature, surfacing the specific papers and passages an answer actually rests on.
Why it's structural, not just similar
Dense search finds similar wording. AetherCV also follows structure.
A plain semantic search matches phrasing — it can miss the paper that matters simply because it doesn’t use the words you did. AetherCV also follows the relationships between papers: what cites what, what extends what, and what shares a benchmark. That structural layer is what lets an answer surface the right prior work even when the wording doesn’t line up.
An illustrative view of how a question connects to related work — not a rendering of the retrieval system itself.
How evidence is verified
Every answer tells you how well-supported it is.
Each answer carries a grounded/low-confidence indicator and a confidence figure, calculated from how directly the retrieved evidence supports the claims made. Color never carries that meaning alone — the badge always pairs an icon with plain text, so you can tell at a glance whether to trust an answer outright or dig into its sources first.
From question to research
One flow, four beats.
Ask
Type a real question about an architecture, a benchmark, or a comparison.
Read a grounded answer
The answer streams in as typeset prose, with a confidence and grounding indicator.
Follow the citations
Click any citation marker to see exactly which paper and passage support that claim.
Save what matters
Keep papers in a local collection you can return to, device-side.
What's built in
Everything a literature review actually needs.
Streamed, grounded answers
Answers stream in as typeset prose with a live grounded/confidence indicator, not a wall of text you have to fact-check yourself.
Citation exploration
Click any citation to trace it back to the paper and passage it comes from, with a satisfying visual thread from claim to source.
Paper relationship view
See which papers surfaced together as evidence across a thread, laid out as an explorable graph.
Side-by-side comparison
Select two or three papers from an answer's evidence and compare them attribute by attribute.
Keyboard-first workflow
A command palette and a full shortcut set make AetherCV fast to drive without ever touching the mouse.
Local research collections
Save papers to a collection that lives on your device, ready whenever you come back to a thread.
Built for how research actually happens
CV / ML researcher
"Which papers actually support each side of the CLIP vs. BLIP comparison?"
Get an answer with distinct, checkable citations per claim — not one blended paragraph.
Grad student, new to a subfield
"What is a vision transformer, and what work did it build on?"
A clear explanation alongside a visible lineage of the related and prior work it came from.
Engineer doing a lit review
"What are the tradeoffs across everything benchmarked on ImageNet-21k?"
A synthesized comparison you can save to a collection and return to later.
From researchers using AetherCV
Testimonial coming soon — reserved for a researcher’s real feedback.
Testimonial coming soon — reserved for a researcher’s real feedback.
Testimonial coming soon — reserved for a researcher’s real feedback.
Questions
Frequently asked
AetherCV grounds its answers in computer vision literature, following both semantic relevance and structural relationships between papers — what cites what, what extends what, and what shares a benchmark. We don't publish a running count of indexed papers here, since that figure changes continuously as the corpus is maintained.
Every answer is checked against the evidence it retrieved before being shown to you, and carries a grounded/low-confidence indicator plus a confidence figure so you can see at a glance how well-supported it is. When the evidence is thin, AetherCV says so rather than filling the gap with a plausible-sounding guess.
AetherCV marks the answer as low-confidence rather than presenting it with the same certainty as a well-supported one. You can still read what it found, but the badge tells you to verify further before relying on it.
Your threads, messages, and usage are your own account's data. Locally-saved features like Collections live only on your device and are never synced anywhere.
Pricing
Coming soon
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Individual
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